UNBIND: UNlearning By INference-time Directional Steering for Code LLMs
Code large language models acquire programming capabilities from large code corpora, but can also memorize implementations that later require removal. Code unlearning is needed to control their continued reproduction when copyright or security concerns arise. However, targeted and retained code share computational patterns, creating a tension between forgetting specific implementations and preserving general programming ability. We propose \textbf{UNBIND}, a code unlearning framework that separately considers which hidden states correspond to the target code and how to suppress its reproduction. By constructing separate directions for these objectives, UNBIND achieves selective unlearning...
| Authors | Zhengyang Shan, Jiayun Xin, Yanjun Lin et al. |
|---|---|
| Primary category | cs.SE |
| Published | 28 September 2026 |
| Last updated at source | 28 September 2026 |
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